1. Technical Field of the Invention
The invention relates generally to communication systems; and, more particularly, it relates to decoding of signals received within such communication systems.
2. Description of Related Art
Data communication systems have been under continual development for many years. One particular type of communication system has received particular attention is a communication system that operates using turbo code.
When performing decoding within such turbo code systems, it is necessary to perform updating of two real sequences during every iteration of the iterative decoding. These two sequences may be viewed as being be viewed as being forward metrics (alphas) and backward metrics (betas) within the context of TCM (Trellis Coded Modulation) decoding as well as TTCM (Turbo Trellis Coded Modulation) decoding. These alphas and betas may be represented as follows: (α0(m),α1(m), . . . , αn−1(m)) and (β0(m),β(m), . . . , βn−1(m)). The updating performed within this iterative decoding is performed using the a posteriori probability and the branch metrics. When performing TTCM decoding, a symbol metric will typically involve more than one information bit (e.g., a plurality of information bits). Therefore, the calculation of the forward metric α(m) and the backward metric β(m) involves more than one computing cycle to calculate all of the possible values of these various information bits of the symbol; this may be characterized as a symbol level decoding approach. However, using the prior art approaches of purely symbol level decoding, such decoding approaches are typically implemented in a manner that costs a lot of transistors (which may be viewed as occupying a great deal of real estate within an integrated circuit that performs the decoding). In addition, a relatively large amount of memory must also typically be dedicated to store all of the calculated values before making final best estimates of the information contained within a received signal. It is also noted that the prior art approaches to performing this purely symbol level decoding is typically performing using the same symbol metric in every iteration of the iterative decoding.
As such, given the relatively large amount of calculations required to perform the prior art symbol level decoding, as well as the relatively large amount of information that must be stored using such symbol level decoding, it would be advantageous to have a decoding approach that could provide for comparable (if not better) performance than symbol level decoding, while also allowing fewer computational steps and a lesser amount of information to be stored before making final best estimates of the information contained within a received signal.